scikit-learn / scikit-learn/scikit-learn
KernelDensity incorrect handling of bandwidth
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Description
Describe the bug
I was using kernel density estimator
https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KernelDensity.html
using 'silverman' or 'scott' as the bandwidth argument. Then I found that the bandwidth automatically adjusted by the algorithm is independent of the actual scale of the dataset. In fact, I was shocked to find that the calculation of a bandwidth in https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/neighbors/_kde.py for 'silverman' and 'scott' does not check the scales of data at all.
Suppose I fit the model kde to some 2D data X and get the bandwidth as kde.bandwidth_.
Next, I fit the model kde to the same 2D data X but with all elements multiplied by, say, 20 and get the bandwidth as kde.bandwidth_.
I found that these two values of kde.bandwidth_ are equal (it is calculated from the shape of X, see the source code). But obviously they should differ by a factor of 20 if the bandwidth is really computed in a truly adaptive manner.
For your reference, I want to mention that scipy's KDE https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.gaussian_kde.html calculates the covariance of data to extract the scale of data. I think this is the right thing to do.
Note that if the bandwidth is incorrect, everything else is incorrect too, including probablities of samples, etc.
Steps/Code to Reproduce
import numpy as np
from sklearn.neighbors import KernelDensity
X = np.random.randn(1000, 2)
kde = KernelDensity(bandwidth='scott')
kde.fit(X)
print(kde.bandwidth_)
kde.fit(X * 20)
print(kde.bandwidth_)
Expected Results
Different bandwidths for data sets with different scales.
Actual Results
0.31622776601683794
0.31622776601683794
Versions
1.2.1
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
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- Open a pull request that references the issue number.
Research direction
Start in sklearn/neighbors/_kde.py and run the provided KernelDensity reproduction with the original and 20x-scaled datasets. Trace how the 'scott' and 'silverman' bandwidths are computed, compare the behavior with the issue's expected scale-dependent result, and verify that the resulting bandwidth and density calculations remain consistent.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- numpy, python
- Domain
- data, machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100